Qualitative models and fuzzy systems: an integrated approach for learning from data
by R. Bellazzi, L. Ironi, R. Guglielmann, M. Stefanelli
in Artificial Intelligence in Medicine , 14, (1998), 5-28.
ABSTRACT
This paper presents a method for the identification of the dynamics of
non-linear systems by learning from data. The key idea which underlies
our approach consists of the integration of qualitative modeling
techniques with fuzzy logic systems. The resulting hybrid method
exploits the a priori structural knowledge on the system
to initialize a fuzzy inference procedure which determines, from the
available experimental data, a functional approximation of the system
dynamics that can be used as a reasonable predictor of the patient's
future state. The major advantage which results from such an
integrated framework lies in a significant improvement of both
efficiency and robustness of identification methods based on fuzzy
models which learn an input-output relation from data. As a benchmark
of our method, we have considered the problem of identifying the
response to the insulin therapy from insulin-dependent diabetic
patients: the results obtained are presented and discussed in the
paper.
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Liliana Ironi
1998